National Enterprise Client
Unified Data Lakehouse — Eliminating Vendor Dependency
A national enterprise was trapped in a fragmented vendor ecosystem where data collection, aggregation, and reporting were handled by three different vendors. Reports took 5-7 days to generate and frequently contained errors with no clear accountability.
5-7 Days → Minutes
Report Delivery Time
60%
Cost Reduction
100%
Data Lineage Visibility
Zero
Vendor Dependencies
The Problem
A classic case of "too many cooks" — but with data vendors instead of chefs.
Fragmented Vendor Ecosystem
Data collection, aggregation, and reporting were handled by three different vendors with no unified ownership.
Report Delays
Business reports took 5-7 days to generate due to coordination between multiple vendors and manual handoffs.
Data Quality Issues
Reports frequently contained errors due to inconsistent transformations and lack of data validation between vendor systems.
High Costs & Finger-Pointing
When issues arose, vendors blamed each other. Troubleshooting required expensive coordination across multiple contracts.
The Vendor Chaos
Vendor A
Data Collection
- •Collected raw data from multiple source systems and APIs
- •No visibility into data quality at source
- •Different data formats with no standardization
Vendor B
Data Aggregation & ETL
- •Transformed and aggregated data from Vendor A
- •Black-box transformations with no documentation
- •Batch processing only — no real-time capabilities
Vendor C
Reporting & Analytics
- •Built dashboards and reports from Vendor B data
- •Limited to pre-built reports with no self-service
- •Couldn't trace errors back to source
The Solution
We designed and built a consolidated data platform that eliminated vendor dependencies and gave the client full ownership of their data pipeline.
ECS-Based Ingestion Layer
Containerized ingestion jobs running on Amazon ECS for both real-time streaming and batch data sources. Scalable, maintainable, and fully managed.
Open Lakehouse Architecture
Apache Iceberg on S3 as the foundation — open table format that prevents vendor lock-in while providing ACID transactions, time travel, and schema evolution.
Polars & PyIceberg Processing
High-performance data processing using Polars for lightning-fast transformations and PyIceberg for native Iceberg table operations. Rust-powered performance without Spark overhead.
Self-Service Analytics
Business users can now build their own reports and explore data without waiting for IT or vendors. Real-time dashboards with drill-down capabilities.
Architecture Overview
Key Benefits
Single Source of Truth
All data flows through one platform with consistent definitions, eliminating discrepancies between vendor systems.
No Vendor Lock-In
Apache Iceberg's open format means data is portable. The client owns their data and can switch tools anytime.
Real-Time + Batch
Unified architecture handles both streaming data and batch feeds in the same pipeline with ECS-based jobs.
Full Transparency
Every data transformation is documented, version-controlled, and traceable from source to report.
Self-Service Analytics
Business users build their own reports without IT bottlenecks or vendor dependencies.
Rust-Powered Performance
Polars delivers 10-100x faster processing than Pandas, enabling rapid iteration and cost savings.
Project Timeline
3 weeks
Discovery & Design
Audit existing vendor systems, map data flows, design target architecture
4 weeks
Foundation Build
Set up AWS infrastructure, Iceberg tables, ingestion pipelines
6 weeks
Migration & Integration
Migrate data sources, build transformations, implement data quality
3 weeks
Analytics & Handoff
Deploy QuickSight dashboards, train users, documentation
Total Project Duration: ~16 weeks
Technology Stack
Ingestion
- Amazon ECS
- ECR
- Step Functions
- EventBridge
Storage
- Amazon S3
- Apache Iceberg
- Parquet
- Lake Formation
Processing
- Polars
- PyIceberg
- Python
- AWS Glue
Analytics
- Amazon Athena
- QuickSight
- QuickSight Q
Trapped in Vendor Dependency?
Let's discuss how a unified data lakehouse can give you control over your data and eliminate costly vendor coordination.